Smart City Gnosys

Smart city article details

Title	Cityflow: Supporting Spatial-Temporal Edge Computing For Urban Machine Learning Applications
ID_Doc	14223
Authors	Kawano M.; Yonezawa T.; Tanimura T.; Giang N.K.; Broadbent M.; Lea R.; Nakazawa J.
Year	2020
Published	EAI/Springer Innovations in Communication and Computing
DOI	http://dx.doi.org/10.1007/978-3-030-28925-6_1
Abstract	A growing trend in smart cities is the use of machine learning techniques to gather city data, formulate learning tasks and models, and use these to develop solutions to city problems. However, although these processes are sufficient for theoretical experiments, they often fail when they meet the reality of city data and processes, which by their very nature are highly distributed, heterogeneous, and exhibit high degrees of spatial and temporal variance. In order to address those problems, we have designed and implemented an integrated development environment called CityFlow that supports developing machine learning applications. With CityFlow, we can develop, deploy, and maintain machine learning applications easily by using an intuitive data flow model. To verify our approach, we conducted two case studies: deploying a road damage detection application to help monitor transport infrastructure and an automatic labeling application in support of a participatory sensing application. These applications show both the generic applicability of our approach, and its ease of use; both critical if we wish to deploy sophisticated ML based applications to smart cities. © 2020, Springer Nature Switzerland AG.
Author Keywords	Edge processing; Participatory sensing; Road damage detection; Smart city; Urban computing

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